This invention relates to tomography and more particularly to the technique known variously as electrical impedance tomography or applied potential tomography referred to hereinafter as EIT.
EIT involves the production of images representing the distribution of an electrical characteristic, such as electrical conductivity or resistivity, across a sectional plane of a body under investigation from measurements made on the periphery of the sectional plane. The technique finds application in non-invasive investigation of human patients, but may be applied to investigation of animals or of other bodies, such as geological masses. It is a relatively inexpensive method of tomography, allows continuous monitoring, and does not suffer from the biological hazards implicit in other procedures such as X-ray computed tomography. The technique is described in, for example, a paper entitled "Applied potential tomography" by D. C. Barber and B. H. Brown published in J.Phys.E: Sci.Instrum., Vol.17 (1984), pages 723-733, and in other papers referred to therein or published subsequently.
In a typical application of EIT to a body an array of, say, sixteen electrodes is placed around the periphery of a body section such as the thorax. Electrical currents, from a constant current source of a few milliamps at a fixed frequency, are applied in turn to adjacent pairs of the electrodes (known as `drive pairs`) and for each applied current the real component of the potential difference is measured between the thirteen adjacent pairs of the fourteen other electrodes (known as `receive pairs`). Further measurements between non-adjacent electrode pairs are not required, as they would not represent independent data but could be obtained by linear combinations of the adjacent measurements. It is of course to be noted that drive pairs and receive pairs need not necessarily be made up of adjacent electrodes and that other combinations of electrodes can be used to gather the set of independent data. The resulting set of voltages from all thirteen receive pairs is referred to as a `data profile`. The measured values from all such data profiles are stored and processed to create a two-dimensional image of the resistivity distribution within the body. A static image may be created, showing the absolute value of tissue resistivity, or a dynamic image may be produced, displaying the changes in resistivity from a reference. The latter is the more clinically useful as changing features of the body such as cardiac activity and lung activity can be monitored.
The EIT image is reconstructed by assuming the measurements have been taken around the periphery of a two dimensional homogenous circular conducting plane. The measured values are filtered to correct for blurring inherent in the imaging process and then backprojected along lines of backprojection to allow determination of the resistivity values within the conducting image plane. The final reconstructed image can then be displayed, the speed of image production depending on the data handling capacity of the image reconstruction system. The technique of backprojection is described more fully in U.S. Pat. No. 4,617,939, to which reference can be made for further details.
The resolution of the image is restricted by the number of independent measurements available, in other words, by the number of electrodes employed. To improve image reconstruction speed, transputers are used for digital signal processing. In addition, the measurements of the voltages in all receive pairs can be made in parallel. Such parallel data collection allows each measurement to be made over a longer period and hence to a higher accuracy. Further details of this system can be found in WO91/19454.
The image reconstruction technique briefly described above produces clinically valuable images. However, it is widely recognised that the reliability of the image is not constant over the entirety of the image plane because of the remoteness of the measuring points from the centre of the body section. The greatest uncertainty is found in the centre of the reconstructed image, since small errors in boundary measurements cause large errors in the reconstructed image data in that central area. In consequence, the signal-to-noise ratio (SNR) of the image is relatively high adjacent to the periphery and decreases towards the centre of the image. As a result, it is difficult to reliably detect small changes in the centre of the image.